Improving Scene Graph Generation with Relation Words' Debiasing in Vision-Language Models

arXiv:2403.16184v1 Announce Type: new
Abstract: Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between various objects. However, this complexity and diversity in SGG also leads to underrepresentation, where part of test triplets are rare or even unseen during training, resulting in imprecise predictions. To tackle this, we propose using the SGG models with pretrained vision-language models (VLMs) to enhance representation. However, due to the gap between the pretraining and …

abstract arxiv basic complexity cs.cv diverse diversity graph however improving language language models leads objects part representation semantics test type underrepresentation vision vision-language models visual words